Deep Learning
Deep learning, a subfield of machine learning, focuses on training artificial neural networks with multiple layers to extract complex patterns from data. Current research emphasizes improving model robustness against noisy or adversarial inputs, exploring efficient architectures like Vision Transformers and convolutional LSTMs for various tasks (e.g., image classification, time series forecasting), and integrating physics-informed approaches for enhanced interpretability and reliability. These advancements are significantly impacting diverse fields, from automated industrial inspection and medical image analysis to improved weather forecasting and more efficient content moderation systems.
3874papers
Papers - Page 51
July 17, 2024
Applying Conditional Generative Adversarial Networks for Imaging Diagnosis
A Scalable and Generalized Deep Learning Framework for Anomaly Detection in Surveillance Videos
Enhancing the Utility of Privacy-Preserving Cancer Classification using Synthetic Data
Hybrid Dynamic Pruning: A Pathway to Efficient Transformer Inference
Virtual Gram staining of label-free bacteria using darkfield microscopy and deep learning
July 15, 2024
Quality Scalable Quantization Methodology for Deep Learning on Edge
Leveraging Bi-Focal Perspectives and Granular Feature Integration for Accurate Reliable Early Alzheimer's Detection
Employing Sentence Space Embedding for Classification of Data Stream from Fake News Domain
Mammographic Breast Positioning Assessment via Deep Learning
Exploring the Potentials and Challenges of Deep Generative Models in Product Design Conception
Comparing Optical Flow and Deep Learning to Enable Computationally Efficient Traffic Event Detection with Space-Filling Curves
Deep ContourFlow: Advancing Active Contours with Deep Learning
July 14, 2024
July 12, 2024
July 11, 2024